ACJIS: A Novel Attentive Cross Approach For Joint Intent Detection And Slot Filling

摘要

Intent detection and slot filling are two important tasks in Spoken Language Understanding. The Condition Random Fields (CRF) was introduced for the tasks pretty much the same fashion to deep neural networks. Recently, attention based encoder-decoder models have shown promising results for joint intent detection and slot filling tasks in spoken language understanding and dialog systems. However, the two tasks are often trained separately. In this paper, we propose ACJIS, a novel Attentive Cross approach for Joint Intent detection and Slot filling. We introduce a cross attention approach to enhance the modeling power on capturing the meaning of word at both tagging level and word level. In order to utilize the information from the two tasks, we leverage multi-task learning to train the model. Our model generates state-of-the-art results on the bench-mark ATIS task. The proposed model also achieves significant gains over the attention based RNN modeling approach for intent detection and slot filling respectively.